Effective Machine Learning Techniques to Detect Fatty Liver Disease

Heart disease, lung disease, respiratory disease, etc. are currently the top killers. The majority of liver problems are difficult to detect early on. One of these is fatty liver disease, a common disorder brought on by a collection of too much liver's fat. Hepatic steatosis is an additional name for it. Alcoholism causes the cells of the liver to accumulate fat. The liver's ability to function is hampered by this. It could result in cancer of the liver and liver damage. Even if a person does not regularly consume alcohol, they can none the less get fatty liver disease. Blood tests, ultrasounds, and computerized tomography scans are the three main types of diagnostic tests. A more precise and dependable, for the early identification of fatty liver disease, an automated software is needed. To anticipate the disease, particular machine learning models are created for this purpose. To identify fatty liver disease with specificity, accuracy, and dependability, methods of Naive Bayes (NB), Random Forest (RF), and eXtreme Gradient Boosting with ANN are proposed in this study. A total of 70000 cases are included in the collection. This classification system is assessed for precision using a confusion matrix.